通过结构优化提升稀疏MLP的性能，效率提升超40%且精度损失低于4%。
A Topological Improvement of the Overall Performance of Sparse Evolutionary Training: Motif-Based Structural Optimization of Sparse MLPs Project
- 基于模式的结构优化改进稀疏进化训练的网络拓扑
- 效率提升超过40%，精度下降小于4%
- 适合关注模型压缩与高效训练的研究者
深度神经网络（DNN）在深度学习多个领域表现出色，但随着模型复杂度增加，降低计算成本和内存开销的需求愈发迫切。稀疏性成为关键解决方案之一。稀疏多层感知机（MLP）在监督特征选择中的鲁棒性，以及稀疏进化训练（SET）的应用，证明了在不牺牲准确率的前提下减少计算开销的可行性。此外，通过称为基于模式的优化的结构优化方法，有望使SET算法效率提升超过40%，性能下降低于4%。本研究探讨将结构优化应用于稀疏进化训练的MLP（SET-MLP）是否能提升性能，以及提升幅度究竟有多大。
原文摘要 · Abstract (English)
Deep Neural Networks (DNNs) have been proven to be exceptionally effective and have been applied across diverse domains within deep learning. However, as DNN models increase in complexity, the demand for reduced computational costs and memory overheads has become increasingly urgent. Sparsity has emerged as a leading approach in this area. The robustness of sparse Multi-layer Perceptrons (MLPs) for supervised feature selection, along with the application of Sparse Evolutionary Training (SET), illustrates the feasibility of reducing computational costs without compromising accuracy. Moreover, it is believed that the SET algorithm can still be improved through a structural optimization method called motif-based optimization, with potential efficiency gains exceeding 40% and a performance decline of under 4%. This research investigates whether the structural optimization of Sparse Evolutionary Training applied to Multi-layer Perceptrons (SET-MLP) can enhance performance and to what extent this improvement can be achieved.
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